Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 73
Sivu ix
... Discriminant functions , 6 1.6 The selection of discriminant functions , 8 1.7 Training methods , 9 1.8 Summary of book by chapters , 11 1.9 Bibliographical and historical remarks , 12 References , 12 2 SOME IMPORTANT DISCRIMINANT ...
... Discriminant functions , 6 1.6 The selection of discriminant functions , 8 1.7 Training methods , 9 1.8 Summary of book by chapters , 11 1.9 Bibliographical and historical remarks , 12 References , 12 2 SOME IMPORTANT DISCRIMINANT ...
Sivu 7
... discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without altering the implied decision surfaces . In general , any monotonic nondecreasing function ( e.g. , logarithmic ) ...
... discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without altering the implied decision surfaces . In general , any monotonic nondecreasing function ( e.g. , logarithmic ) ...
Sivu 16
... discriminant func- tion . A complete specification of any linear discriminant function is achieved by specifying the values of the weights or parameters of the function family . A pattern classifier employing linear discriminant functions ...
... discriminant func- tion . A complete specification of any linear discriminant function is achieved by specifying the values of the weights or parameters of the function family . A pattern classifier employing linear discriminant functions ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |